Road rage monitoring method, system and device based on facial and respiratory characteristics and medium
A facial feature and face technology, applied in computer parts, character and pattern recognition, instruments, etc., can solve problems such as increasing the driver's burden, driver's discomfort, affecting the driver's normal driving behavior, etc., to improve the recognition rate and robustness, high reliability effect
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Embodiment 1
[0040] Such as figure 1 As shown, the road rage detection method based on facial and respiratory features includes:
[0041] Obtain the driver's facial video and breathing data;
[0042] Extracting facial region images from facial videos, and extracting facial features from acquired facial region images;
[0043] extracting breath features from the acquired breath data;
[0044] Perform feature fusion on the collected facial features and breathing features;
[0045] Input the fused features into the trained deep learning model, and output the road rage monitoring status.
[0046] As an embodiment, the specific steps of extracting the face area image from the face video are:
[0047] Select a facial image with a set duration, extract a frame of facial images at intervals of a set time period, extract a total of several frames of facial images, and perform smoothing and denoising processing on each frame of facial images extracted;
[0048] For several frames of images afte...
Embodiment 2
[0119] Embodiment 2: A road rage monitoring system based on facial and breathing features is provided;
[0120] Road rage monitoring system based on facial and breathing features, including:
[0121] Acquisition module, used to obtain the driver's facial video and breathing data;
[0122] The facial feature extraction module is used to extract the facial area image from the facial video, and extracts the facial features from the acquired facial area image;
[0123] Breath feature extraction module, for extracting breath feature from the breath data that obtains;
[0124] The feature fusion module is used to perform feature fusion on the collected facial features and respiratory features;
[0125] The road rage status monitoring module is used to input the fused features into the trained deep learning model and output the road rage monitoring status.
Embodiment 3
[0126] Embodiment 3: This embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, each step in the method is completed. For the sake of brevity, the operation will not be repeated here.
[0127] Described electronic device can be mobile terminal and non-mobile terminal, and non-mobile terminal comprises desktop computer, and mobile terminal comprises smart phone (Smart Phone, such as Android mobile phone, IOS mobile phone etc.), smart glasses, smart watch, smart bracelet, tablet computer , laptops, personal digital assistants and other mobile Internet devices that can communicate wirelessly.
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